Automatic Loop Kernel Analysis and Performance Modeling With Kerncraft

TL;DR

Kerncraft is a tool that automates the creation of analytic performance models for loop kernels, helping developers understand hardware bottlenecks and optimize code efficiently.

Contribution

It introduces Kerncraft, a tool that simplifies and accelerates the construction of performance models for streaming kernels and stencil loops from source code and hardware info.

Findings

Kerncraft can predict single-core performance.

It models scaling behavior on multicore processors.

It uses Roofline and ECM models for analysis.

Abstract

Analytic performance models are essential for understanding the performance characteristics of loop kernels, which consume a major part of CPU cycles in computational science. Starting from a validated performance model one can infer the relevant hardware bottlenecks and promising optimization opportunities. Unfortunately, analytic performance modeling is often tedious even for experienced developers since it requires in-depth knowledge about the hardware and how it interacts with the software. We present the "Kerncraft" tool, which eases the construction of analytic performance models for streaming kernels and stencil loop nests. Starting from the loop source code, the problem size, and a description of the underlying hardware, Kerncraft can ideally predict the single-core performance and scaling behavior of loops on multicore processors using the Roofline or the Execution-Cache-Memory (ECM) model. We describe the operating principles of Kerncraft with its capabilities and limitations, and we show how it may be used to quickly gain insights by accelerated analytic modeling.